pysec - pysec-2020-297

pysec-2020-297

Vulnerability from pysec

Published

2020-12-10 23:15

Modified

2021-12-09 06:34

Details

In affected versions of TensorFlow under certain cases a saved model can trigger use of uninitialized values during code execution. This is caused by having tensor buffers be filled with the default value of the type but forgetting to default initialize the quantized floating point types in Eigen. This is fixed in versions 1.15.5, 2.0.4, 2.1.3, 2.2.2, 2.3.2, and 2.4.0.

Aliases

JSON

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{
  "affected": [
    {
      "package": {
        "ecosystem": "PyPI",
        "name": "tensorflow-cpu",
        "purl": "pkg:pypi/tensorflow-cpu"
      },
      "ranges": [
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            },
            {
              "fixed": "ace0c15a22f7f054abcc1f53eabbcb0a1239a9e2"
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          "repo": "https://github.com/tensorflow/tensorflow",
          "type": "GIT"
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        {
          "events": [
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            },
            {
              "fixed": "1.15.5"
            },
            {
              "introduced": "2.0.0"
            },
            {
              "fixed": "2.0.4"
            },
            {
              "introduced": "2.1.0"
            },
            {
              "fixed": "2.1.3"
            },
            {
              "introduced": "2.2.0"
            },
            {
              "fixed": "2.2.2"
            },
            {
              "introduced": "2.3.0"
            },
            {
              "fixed": "2.3.2"
            }
          ],
          "type": "ECOSYSTEM"
        }
      ],
      "versions": [
        "1.15.0",
        "2.1.0",
        "2.1.1",
        "2.1.2",
        "2.2.0",
        "2.2.1",
        "2.3.0",
        "2.3.1"
      ]
    }
  ],
  "aliases": [
    "CVE-2020-26266",
    "GHSA-qhxx-j73r-qpm2"
  ],
  "details": "In affected versions of TensorFlow under certain cases a saved model can trigger use of uninitialized values during code execution. This is caused by having tensor buffers be filled with the default value of the type but forgetting to default initialize the quantized floating point types in Eigen. This is fixed in versions 1.15.5, 2.0.4, 2.1.3, 2.2.2, 2.3.2, and 2.4.0.",
  "id": "PYSEC-2020-297",
  "modified": "2021-12-09T06:34:44.221678Z",
  "published": "2020-12-10T23:15:00Z",
  "references": [
    {
      "type": "FIX",
      "url": "https://github.com/tensorflow/tensorflow/commit/ace0c15a22f7f054abcc1f53eabbcb0a1239a9e2"
    },
    {
      "type": "ADVISORY",
      "url": "https://github.com/tensorflow/tensorflow/security/advisories/GHSA-qhxx-j73r-qpm2"
    }
  ]
}

ghsa-qhxx-j73r-qpm2

Vulnerability from github

Published

2020-12-10 19:07

Modified

2024-10-28 19:57

Severity ?

4.4 (Medium) - CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:N/I:L/A:L
4.8 (Medium) - CVSS:4.0/AV:L/AC:L/AT:N/PR:L/UI:N/VC:N/VI:L/VA:L/SC:N/SI:N/SA:N

Summary

Uninitialized memory access in TensorFlow

Details

Impact

Under certain cases, a saved model can trigger use of uninitialized values during code execution. This is caused by having tensor buffers be filled with the default value of the type but forgetting to default initialize the quantized floating point types in Eigen:

```cc struct QUInt8 { QUInt8() {} // ... uint8_t value; };

struct QInt16 { QInt16() {} // ... int16_t value; };

struct QUInt16 { QUInt16() {} // ... uint16_t value; };

struct QInt32 { QInt32() {} // ... int32_t value; }; ```

Patches

We have patched the issue in GitHub commit ace0c15a22f7f054abcc1f53eabbcb0a1239a9e2 and will release TensorFlow 2.4.0 containing the patch. TensorFlow nightly packages after this commit will also have the issue resolved.

Since this issue also impacts TF versions before 2.4, we will patch all releases between 1.15 and 2.3 inclusive.

For more information

Please consult our security guide for more information regarding the security model and how to contact us with issues and questions.

Show details on source website

JSON

To clipboard

{
  "affected": [
    {
      "package": {
        "ecosystem": "PyPI",
        "name": "tensorflow"
      },
      "ranges": [
        {
          "events": [
            {
              "introduced": "0"
            },
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          ],
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    {
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        "ecosystem": "PyPI",
        "name": "tensorflow"
      },
      "ranges": [
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            },
            {
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            {
              "introduced": "2.1.0"
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            {
              "fixed": "2.1.3"
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            {
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      "ranges": [
        {
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            {
              "introduced": "2.3.0"
            },
            {
              "fixed": "2.3.2"
            }
          ],
          "type": "ECOSYSTEM"
        }
      ]
    },
    {
      "package": {
        "ecosystem": "PyPI",
        "name": "tensorflow-cpu"
      },
      "ranges": [
        {
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        "name": "tensorflow-cpu"
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              "introduced": "2.1.0"
            },
            {
              "fixed": "2.1.3"
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        }
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        "name": "tensorflow-cpu"
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      "ranges": [
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          "events": [
            {
              "introduced": "2.2.0"
            },
            {
              "fixed": "2.2.2"
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          ],
          "type": "ECOSYSTEM"
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      ]
    },
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      "package": {
        "ecosystem": "PyPI",
        "name": "tensorflow-cpu"
      },
      "ranges": [
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          "events": [
            {
              "introduced": "2.3.0"
            },
            {
              "fixed": "2.3.2"
            }
          ],
          "type": "ECOSYSTEM"
        }
      ]
    },
    {
      "package": {
        "ecosystem": "PyPI",
        "name": "tensorflow-gpu"
      },
      "ranges": [
        {
          "events": [
            {
              "introduced": "0"
            },
            {
              "fixed": "1.15.5"
            }
          ],
          "type": "ECOSYSTEM"
        }
      ]
    },
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        "ecosystem": "PyPI",
        "name": "tensorflow-gpu"
      },
      "ranges": [
        {
          "events": [
            {
              "introduced": "2.0.0"
            },
            {
              "fixed": "2.0.4"
            }
          ],
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        }
      ]
    },
    {
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        "name": "tensorflow-gpu"
      },
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        {
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            },
            {
              "fixed": "2.1.3"
            }
          ],
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        }
      ]
    },
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        "name": "tensorflow-gpu"
      },
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        {
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            {
              "introduced": "2.2.0"
            },
            {
              "fixed": "2.2.2"
            }
          ],
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      ]
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        "ecosystem": "PyPI",
        "name": "tensorflow-gpu"
      },
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        {
          "events": [
            {
              "introduced": "2.3.0"
            },
            {
              "fixed": "2.3.2"
            }
          ],
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        }
      ]
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  ],
  "aliases": [
    "CVE-2020-26266"
  ],
  "database_specific": {
    "cwe_ids": [
      "CWE-908"
    ],
    "github_reviewed": true,
    "github_reviewed_at": "2020-12-10T19:04:48Z",
    "nvd_published_at": null,
    "severity": "MODERATE"
  },
  "details": "### Impact\nUnder certain cases, a saved model can trigger use of uninitialized values during code execution. This is caused by having tensor buffers be filled with the default value of the type but forgetting to [default initialize the quantized floating point types in Eigen](https://github.com/tensorflow/tensorflow/blob/f70160322a579144950dff1537dcbe3c7c09d6f5/third_party/eigen3/unsupported/Eigen/CXX11/src/FixedPoint/FixedPointTypes.h#L61-L104):\n\n```cc\nstruct QUInt8 {\n  QUInt8() {}\n  // ...\n  uint8_t value;\n};\n\nstruct QInt16 {\n  QInt16() {}\n  // ...\n  int16_t value;\n};\n\nstruct QUInt16 {\n  QUInt16() {}\n  // ...\n  uint16_t value;\n};\n\nstruct QInt32 {\n  QInt32() {}\n  // ...\n  int32_t value;\n};\n```\n\n### Patches\nWe have patched the issue in GitHub commit [ace0c15a22f7f054abcc1f53eabbcb0a1239a9e2](https://github.com/tensorflow/tensorflow/commit/ace0c15a22f7f054abcc1f53eabbcb0a1239a9e2) and will release TensorFlow 2.4.0 containing the patch. TensorFlow nightly packages after this commit will also have the issue resolved.\n\nSince this issue also impacts TF versions before 2.4, we will patch all releases between 1.15 and 2.3 inclusive.\n\n### For more information\nPlease consult [our security guide](https://github.com/tensorflow/tensorflow/blob/master/SECURITY.md) for more information regarding the security model and how to contact us with issues and questions.",
  "id": "GHSA-qhxx-j73r-qpm2",
  "modified": "2024-10-28T19:57:07Z",
  "published": "2020-12-10T19:07:24Z",
  "references": [
    {
      "type": "WEB",
      "url": "https://github.com/tensorflow/tensorflow/security/advisories/GHSA-qhxx-j73r-qpm2"
    },
    {
      "type": "ADVISORY",
      "url": "https://nvd.nist.gov/vuln/detail/CVE-2020-26266"
    },
    {
      "type": "WEB",
      "url": "https://github.com/tensorflow/tensorflow/commit/ace0c15a22f7f054abcc1f53eabbcb0a1239a9e2"
    },
    {
      "type": "WEB",
      "url": "https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-cpu/PYSEC-2020-297.yaml"
    },
    {
      "type": "WEB",
      "url": "https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-gpu/PYSEC-2020-332.yaml"
    },
    {
      "type": "WEB",
      "url": "https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow/PYSEC-2020-254.yaml"
    },
    {
      "type": "PACKAGE",
      "url": "https://github.com/tensorflow/tensorflow"
    }
  ],
  "schema_version": "1.4.0",
  "severity": [
    {
      "score": "CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:N/I:L/A:L",
      "type": "CVSS_V3"
    },
    {
      "score": "CVSS:4.0/AV:L/AC:L/AT:N/PR:L/UI:N/VC:N/VI:L/VA:L/SC:N/SI:N/SA:N",
      "type": "CVSS_V4"
    }
  ],
  "summary": "Uninitialized memory access in TensorFlow"
}

cve-2020-26266

Vulnerability from cvelistv5

Published

2020-12-10 22:10

Modified

2024-08-04 15:56

Severity ?

4.4 (Medium) - CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:N/I:L/A:L

Summary

References

▼	URL	Tags
	https://github.com/tensorflow/tensorflow/security/advisories/GHSA-qhxx-j73r-qpm2	x_refsource_CONFIRM
	https://github.com/tensorflow/tensorflow/commit/ace0c15a22f7f054abcc1f53eabbcb0a1239a9e2	x_refsource_MISC

Impacted products

Vendor

Product

Version

▼

tensorflow

Version: < 1.15.5
Version: >= 2.0.0, < 2.0.4
Version: >= 2.1.0, < 2.1.3
Version: >= 2.2.0, < 2.2.2
Version: >= 2.3.0, < 2.3.2

Show details on NVD website

JSON

To clipboard

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Sightings

Author	Source	Type	Date

Nomenclature

Seen: The vulnerability was mentioned, discussed, or seen somewhere by the user.
Confirmed: The vulnerability is confirmed from an analyst perspective.
Exploited: This vulnerability was exploited and seen by the user reporting the sighting.
Patched: This vulnerability was successfully patched by the user reporting the sighting.
Not exploited: This vulnerability was not exploited or seen by the user reporting the sighting.
Not confirmed: The user expresses doubt about the veracity of the vulnerability.
Not patched: This vulnerability was not successfully patched by the user reporting the sighting.

Action not permitted

pysec-2020-297

Vulnerability from pysec

ghsa-qhxx-j73r-qpm2

Vulnerability from github

Impact

Patches

For more information

cve-2020-26266

Vulnerability from cvelistv5

Tags

Sightings

Nomenclature